Data incorporating large quantities of variables is becoming increasingly commonplace, especially in data sets that are sufficiently large that they may be generated and/or stored by multiple computing devices. In addition to the challenges of handling such a large quantity of data, increasing the quantity of variables in a data set by even a small degree tends to add exponentially to at least the complexity of relationships among the data values, and may result in an exponential increase in data size.
Among such challenging data sets are large random samples generated by various forms of statistical analysis. Performance testing is essential for quality assurance of products and services across all industries. A reliable performance testing depends largely on proper testing data, which is not always accessible for testing purposes. Accordingly, developers and manufacturers are challenged with providing testing data for testing products and services where such testing data may not be obtainable. As a result, precision of the testing results is often inaccurate or misleading since the performance testing data was not available.